Computational space reduction and parallelization of a new clustering approach for large groups of sequences

• Published in: Bioinformatics (Oxford, England) Vol. 14; no. 5; pp. 439 - 451
• Main Authors: Trelles, O, Andrade, M.A, Valencia, A, Zapata, E.L, Carazo, J.M
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.06.1998
• Subjects: Algorithms; amino acid sequences; Biological and medical sciences; Cluster Analysis; Computational Biology; Databases, Factual; Evaluation Studies as Topic; Fundamental and applied biological sciences. Psychology; General aspects; Genome; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); nucleotide sequences; Proteins - genetics; Sequence Alignment - methods; Sequence Alignment - statistics & numerical data; Parallel algorithm; Nucleotide sequence; Computerized processing; Database; Software; Nucleic acid; Parallelization; Protein; Aminoacid sequence
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/14.5.439

The explosive growth of the biological sequences databases stimulated by genome projects has modified the framework of several applications in the biological sequence analysis area. In most cases, this new scenario is characterized by studies on large sets of sequences, suggesting the need for effective and automatic methods for their clustering. A more effective clustering of the database could be followed by the application of common family analysis schemes to the groups so formed. In this work, we present a new strategy to reduce the computational cost associated with the clustering of large sets of sequences which are expected to contain several families. The strategy is based on the grouping of the sequences into families by using a dynamic threshold on a pairwise sequence similarity criterion. Routine clustering of large data sets can now be done very efficiently. The method developed here achieves a computational space reduction of about an order of magnitude over more traditional ones of all-versus-all comparisons. The outcome of this approach produces family groupings that reproduce closely already accepted biological results. Our work includes a parallel implementation for distributed memory multiprocessors with a dynamic scheduling strategy for performance optimization. By anonymous ftp at ftp.ac.uma.es (/pub/ots/pCluster directory), or from our Web site http://www.cnb. uam.es/www/software/software_index.html ots@ac.uma.es